Machine learning-based prediction of postoperative mortality risk after abdominal surgery.

Abstract

Background: Preoperative risk assessments are vital for identifying patients at high risk of postoperative mortality. However, traditional scoring systems can be time consuming. We hypothesized that the use of machine learning models would enable rapid and accurate risk assessments to be performed.

Aim: To assess the potential of machine learning algorithms to develop predictive models of mortality risk after abdominal surgery.

Methods: This retrospective study included 230 individuals who underwent abdominal surgery at the Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine between January 2023 and December 2023. Demographic and surgery-related data were collected and used to develop nomogram, decision-tree, random-forest, gradient-boosting, support vector machine, and naïve Bayesian models to predict 30-day mortality risk after abdominal surgery. Models were assessed using receiver operating characteristic curves and compared using the DeLong test.

Results: Of the 230 included patients, 52 died and 178 survived. Models were developed using the training cohort (n = 161) and assessed using the validation cohort (n = 68). The areas under the receiver operating characteristic curves for the nomogram, decision-tree, random-forest, gradient-boosting tree, support vector machine, and naïve Bayesian models were 0.908 [95% confidence interval (CI): 0.824-0.992], 0.874 (95%CI: 0.785-0.963), 0.928 (95%CI: 0.869-0.987), 0.907 (95%CI: 0.837-0.976), 0.983 (95%CI: 0.959-1.000), and 0.807 (95%CI: 0.702-0.911), respectively.

Conclusion: Nomogram, random-forest, gradient-boosting tree, and support vector machine models all demonstrate strong performances for the prediction of postoperative mortality and can be selected based on the clinical circumstances.